Fatigue damage prognostics and life prediction with dynamic response reconstruction using indirect sensor measurements

This study presents a general methodology for fatigue damage prognostics and life prediction integrating the structural health monitoring system. A new method for structure response reconstruction of critical locations using measurements from remote sensors is developed. The method is based on the empirical mode decomposition with intermittency criteria and transformation equations derived from finite element modeling. Dynamic responses measured from usage monitoring system or sensors at available locations are decomposed into modal responses directly in time domain. Transformation equations based on finite element modeling are used to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. The mode superposition method is employed to obtain dynamic responses at critical locations for fatigue crack propagation analysis. Fatigue analysis and life prediction can be performed given reconstructed responses at the critical location. The method is demonstrated using a multi degree-of-freedom cantilever beam problem. DOI: 10.4018/978-1-4666-2095-7.ch019

[1]  M. Haase,et al.  Damage identification based on ridges and maxima lines of the wavelet transform , 2003 .

[2]  N. Huang,et al.  System identification of linear structures based on Hilbert–Huang spectral analysis. Part 1: normal modes , 2003 .

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  Zhiyong Tao,et al.  Extraction of time varying information from noisy signals: An approach based on the empirical mode decomposition , 2011 .

[5]  Greg Welsh,et al.  A structural integrity prognosis system , 2009 .

[6]  Asok Ray,et al.  A State-Space Model of Fatigue Crack Growth , 1998 .

[7]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[8]  T. R. Porter,et al.  Method of analysis and prediction for variable amplitude fatigue crack growth , 1972 .

[9]  Jennifer E. Michaels,et al.  Sensors for monitoring early stage fatigue cracking , 2007 .

[10]  Ahsan Kareem,et al.  Applications of wavelet transforms in earthquake, wind and ocean engineering , 1999 .

[11]  Xinqun Zhu,et al.  Time-varying system identification using a newly improved HHT algorithm , 2009 .

[12]  Robert X. Gao,et al.  Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring , 2006, IEEE Transactions on Instrumentation and Measurement.

[13]  Yongming Liu,et al.  An incremental crack growth model for multi-scale fatigue analysis , 2009 .

[14]  Douglas E. Adams,et al.  A nonlinear dynamical systems framework for structural diagnosis and prognosis , 2002 .

[15]  J. Beck,et al.  Estimation of Small Failure Probabilities in High Dimensions by Subset Simulation , 2001 .

[16]  Shalabh Gupta,et al.  Online fatigue damage monitoring by ultrasonic measurements : A symbolic dynamics approach , 2007 .

[17]  Crinela Pislaru,et al.  Modal parameter identification for CNC machine tools using Wavelet Transform , 2003 .

[18]  Michael Link,et al.  Damage identification by multi-model updating in the modal and in the time domain , 2009 .

[19]  Jiubin Tan,et al.  Identification of modal parameters of a system with high damping and closely spaced modes by combining continuous wavelet transform with pattern search , 2008 .

[20]  Chih-Chen Chang,et al.  Identification of nonlinear elastic structures using empirical mode decomposition and nonlinear normal modes , 2007 .

[21]  P. C. Paris,et al.  A Critical Analysis of Crack Propagation Laws , 1963 .

[22]  Michael Feldman,et al.  NON-LINEAR FREE VIBRATION IDENTIFICATION VIA THE HILBERT TRANSFORM , 1997 .

[23]  Robert I. Damper,et al.  Non-parametric linear time-invariant system identification by discrete wavelet transforms , 2006, Digit. Signal Process..

[24]  Zizi Lu,et al.  Small time scale fatigue crack growth analysis , 2010 .